Fetching the paper…
Reading the bibliography…
Recurrent Neural Networks (RNNs) produce state-of-art performance on many machine learning tasks but their demand on resources in terms of memory and computational power are often high.
Tidigits Ldc93S10, 1993
R G Leonard and G Doddington · 1993
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter, S Hochreiter, Jürgen Schmidhuber, and J Schmidhuber · 1997
Earlier work this paper cites.
the Penn Treebank: an Overview
Ann Taylor, Mitchell Marcus, and Beatrice Santorini · 2003
Earlier work this paper cites.
Connectionist Temporal Classification : Labelling unsegmented sequence data with recurrent neural networks
Alex Graves, Santiago Fernandez, Faustino Gomez, and Jurgen Schmidhuber · 2006
Earlier work this paper cites.
Speech recognition with weighted finite-state transducers
Mehryar Mohri, Fernando Pereira, and Michael Riley · 2008
Earlier work this paper cites.
Neuflow: A runtime reconfigurable dataflow processor for vision
C. Farabet, B. Martini, B. Corda, P. Akselrod, E. Culurciello, and Y. LeCun · 2011
Earlier work this paper cites.
The Kaldi Speech Recognition Toolkit, 2011
Daniel Povey, Arnab Ghoshal, Gilles Boulianne, Lukas Burget, Ondrej Glembek, Nagendra Goel, Mirko Hannemann, Petr Motlicek, Yanmin Qian, Petr Schwarz, Jan Silovsky, Georg Stemmer, and Karel Vesely · 2011
Earlier work this paper cites.
Subword language modeling with neural networks
Tomáš Mikolov, Ilya Sutskever, Anoop Deoras, Hai-Son Le, Stefan Kombrink, and J Cernocky · 2012
Earlier work this paper cites.
On the properties of neural machine translation: Encoder-decoder approaches
Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
Earlier work this paper cites.
Fast and robust neural network joint models for statistical machine translation
Jacob Devlin, Rabih Zbib, Zhongqiang Huang, Thomas Lamar, Richard Schwartz, and John Makhoul · 2014
Earlier work this paper cites.
Towards end-to-end speech recognition with recurrent neural networks
Alex Graves and Navdeep Jaitly · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
Cited alongside, same era.
Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
Cited alongside, same era.
Deep-Speech 2: End-to-end speech recognition in English and Mandarin
Dario Amodei, Rishita Anubhai, Eric Battenberg, Carl Case, Jared Casper, Bryan Catanzaro, Jingdong Chen, Mike Chrzanowski, Adam Coates, Greg Diamos, et al · 2015
Cited alongside, same era.
William Chan, Navdeep Jaitly, Quoc V. Le, and Oriol Vinyals · 2015
Cited alongside, same era.
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms
Evangelos Stromatias, Daniel Neil, Michael Pfeiffer, Francesco Galluppi, Steve B Furber, and Shih-Chii Liu · 2015
Later among the works it cites.
Blocks and Fuel : Frameworks for deep learning
Bart van Merrienboer, Dzmitry Bahdanau, Vincent Dumoulin, Dmitriy Serdyuk, David Warde-farley, Jan Chorowski, and Yoshua Bengio · 2015
Later among the works it cites.
A character-level decoder without explicit segmentation for neural machine translation
Junyoung Chung, Kyunghyun Cho, and Yoshua Bengio · 2016
Closest in time.
BinaryNet: Training deep neural networks with weights and activations constrained to +1 or -1
Matthieu Courbariaux and Yoshua Bengio · 2016
Closest in time.
XNOR-Net: ImageNet classification using binary convolutional neural networks
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
Cited alongside, same era.
Batch Normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
Cited alongside, same era.
Regularizing RNNs by Stabilizing Activations
David Krueger and Roland Memisevic · 2015
Cited alongside, same era.
Neural Networks with Few Multiplications
Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, and Yoshua Bengio · 2015
Cited alongside, same era.
EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding
Yajie Miao, Mohammad Gowayyed, and Florian Metze · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 2016
Closest in time.
Fixed-point performance analysis of recurrent neural networks
Sungho Shin, Kyuyeon Hwang, and Wonyong Sung · 2016
Closest in time.
Mastering the game of Go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Closest in time.
A 1.42TOPS/W deep convolutional neural network recognition processor for intelligent IoE systems
J. Sim, J. S. Park, M. Kim, D. Bae, Y. Choi, and L. S. Kim · 2016
Closest in time.
Theano: A {Python} framework for fast computation of mathematical expressions
Theano Development Team · 2016
Closest in time.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2059
Closest in time.